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 "metadata": {
  "name": "",
  "signature": "sha256:b88a47a1f89a10e1a5840434f66c1ccadb556b4fe54eb378ff1c9149e2521b9b"
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Import standard libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pylab as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Import the package MultiNetX"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import multinetx as mx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Create two Erd\"os- R'enyi networks with N nodes for each layer"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 20\n",
      "g1 = mx.barabasi_albert_graph(N,2,seed=231)\n",
      "g2 = mx.barabasi_albert_graph(N,3,seed=231)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Create an 2Nx2N lil sparse matrix for interconnecting the two layers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "adj_block = mx.lil_matrix(np.zeros((N*2,N*2)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Define the type of interconnection between the layers (here we use identity matrices thus connecting one-to-one the nodes between layers)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "adj_block[:N,N:] = np.identity(N)    # L_12\n",
      "adj_block += adj_block.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Create an instance of the MultilayerGraph class"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mg = mx.MultilayerGraph(list_of_layers=[g1,g2],\n",
      "                        inter_adjacency_matrix=adj_block)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Inter-layer weights (intra-layer weight equals one)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "step = 0.01\n",
      "total_steps = 10000\n",
      "eigval_all = np.zeros((total_steps,mg.number_of_nodes()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Loop for scanning inter-layer weight with ::step for ::total_steps"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for n in range(total_steps):\n",
      "    inter_diff = step * n\n",
      "    diffusion_constants = mg.set_edges_weights(intra_layer_edges_weight=1.0,\n",
      "                                        inter_layer_edges_weight=inter_diff)\n",
      "    # Laplacian spectrum        \n",
      "    eigval_all[n] = mx.laplacian_spectrum(mg,weight=\"weight\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Plot the eigenvalues as a function of inter-layer coupling"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "fig.clf()\n",
      "ax = fig.add_subplot(111)\n",
      "ax.tick_params(axis='both',which='major',labelsize=14)\n",
      "ax.set_title('Spectrum of ' + mg.name,fontsize=10)\n",
      "ax.set_xlabel('$D_x$',fontsize=14)\n",
      "ax.set_ylabel('$\\lambda_2$,...,$\\lambda_{'+\\\n",
      "                '{}'.format(N*mg.get_number_of_layers())+\\\n",
      "                '}$',fontsize=14)\n",
      "ax.set_xscale('log')\n",
      "ax.set_yscale('log')\n",
      "ax.set_ylim(bottom=step,top=total_steps)\n",
      "ax.set_xlim(step,step*total_steps)\n",
      "ax.plot([step*n for n in range(total_steps)],eigval_all,linewidth=1)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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QjqrSua2Thr800PjXRrr2hYlcHeLZ+bX8cpHNeQd2s3jTKqp3x3n+aBmXTtnP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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe2d9b5c750>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mg.get_number_of_layers()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "2"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    }
   ],
   "metadata": {}
  }
 ]
}